Today baseball players use advanced analytics on pitchers, strike zones, and hitting to try to improve their averages. Like baseball, insurers now have access to tremendously more data than ever before, yet little focus has been placed on using it effectively to improve the hit ratio.

The recent Accenture report, [marketo-rtp-id id=”rtp-form-id” image=”” description=”” title=”Harnessing the Data Exhaust Stream: Changing the Way the Insurance Game is Played” registration_page_link=””], explores how carriers can use the abundant amount of data that is now available to drive and improve profitability. Building off these insights there are specific actions carriers can take to improve hit ratio performance using data and analytics.

1. Going After the Risk they Want

With the digital revolution data has become much more accessible. Insurers can now collect and analyze data from a wide range of sources and develop insights that were once hidden. Insurers can then use these insights to go after the risk they actually want and have a higher return on acquisition dollars invested. For example:

  • Targeted Marketing: Insurers can perform analysis based off internal and external data to determine the behaviors that make up the low risk profiles based on criteria from each line of business. Once these micro segments are established, they can specifically target the risk they willing to take by creating a more direct and personalized marketing campaign for each of the targeted segments.
  • Point Of Sale Analysis: By performing a POS analysis, insurers can determine which channels are worth investing acquisition dollars. Once they understand how the targeted customers are buying they can create more effective distribution strategy.
  • IoT: Through leveraging the data from connected devices insurers can incentivize policyholders to share data with them. They can then used this data to reward customers for low risk behaviors (active fitbit users); or monitor it in real time (moving carriers from a risk pretention position to risk prevention position) in order to react in high risk situations to prevent claims before they even happen (homes that use smart devices such as Nest).

2. Predictive Modeling

With the amount of available data shifting the industry from generic profiles to micro segments, insurance companies can now meet the specific needs and expectations of more customers through the use of predictive modeling. Predictive modeling will enable insurers to create new products as well as match customers to relevant existing products and services.

With the ability to analyze structured and unstructured data from a range of sources, insurers now can better understand customer’s lifestyles, behaviors, needs and expectations. This information will also help to develop predictions models that can pinpoint driving factors for retention and how needs may change or grow. For example, if insurers know a potential customer is a recent college graduate who is very price sensitive and a millennial who prefers digital channels, they can create new products or levels of coverage that are tailor specifically to them. By creating new products or adjusting the level of coverage for the price sensitive customer, but also taking into account the digital preference, insurers could potentially capture a bigger share of the market while reducing their need to hire more employees. This is because with the use of micro segments, predictive models are able to better predict behaviors. Customers who are millennials and prefer digital communication are more likely to file claims digitally and be handled through straight through processing.

As this customer grows so does the history of the data. As this college graduate slowly approaches 30, insurers now have the opportunity to use predictive modeling to cross or upsell other products that match their current lifestyle, behaviors, needs and expectations.

3. Augmented Intelligence

Analytics doesn’t stop there. With the use of augmented intelligence insurers are now able to match the best underwriter with the right sales opportunity. By training machine learning or artificial intelligence solutions with which underwriters are getting the best price, hit ratio, and performance for given risk types, the system can consistently match the underwriter with the best chance of success.

So you not only get to target the right customer, match that with the right solution, but you can also line up your best resource. If you can’t improve your hit ratio with that combination…well then maybe baseball isn’t your game.

Special thanks to Cameron Goldade who helped me with this post.

Learn more by reading Harnessing the Data Exhaust Stream: Changing the Way the Insurance Game is Played:

[marketo-rtp-id id=”rtp-form-id” image=”” description=”Read more about how external insurance data and analytics is changing everything, from pricing risk to interacting with customers.” title=”Harnessing the Data Exhaust Stream: Changing the Way the Insurance Game is Played” registration_page_link=””]

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